Univariate Data Analysis¶

Need for data visualisation¶

In [1]:
from warnings import filterwarnings
filterwarnings('ignore')
In [2]:
import os
os.chdir('E:/Datasets/')
In [3]:
import pandas as pd
df=pd.read_csv('50_Startups (1).csv')
df.head()
Out[3]:
RND ADMIN MKT STATE PROFIT
0 165349.20 136897.80 471784.10 New York 192261.83
1 162597.70 151377.59 443898.53 California 191792.06
2 153441.51 101145.55 407934.54 Florida 191050.39
3 144372.41 118671.85 383199.62 New York 182901.99
4 142107.34 91391.77 366168.42 Florida 166187.94
In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 5 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   RND     50 non-null     float64
 1   ADMIN   50 non-null     float64
 2   MKT     50 non-null     float64
 3   STATE   50 non-null     object 
 4   PROFIT  50 non-null     float64
dtypes: float64(4), object(1)
memory usage: 2.1+ KB
In [5]:
df.shape
Out[5]:
(50, 5)
In [6]:
####for checking missing values
df.isna().sum()
Out[6]:
RND       0
ADMIN     0
MKT       0
STATE     0
PROFIT    0
dtype: int64

Visualizing RND and PROFIT¶

In [7]:
import matplotlib.pyplot as plt
import seaborn as sns

using matplotlib library¶

In [8]:
plt.figure(figsize=(16,8))
plt.scatter(df['RND'] , df['PROFIT'])
plt.xlabel('RND')
plt.ylabel('PROFIT')
plt.title('RND VS PROFIT')
plt.grid()
plt.show()

RND and PROFIT are Linearly Related and as RND increases , PROFIT also Increase¶

In [9]:
# using seaborn library
In [10]:
plt.figure(figsize=(16,8))
sns.scatterplot(data=df,x='RND' ,y='PROFIT')
plt.title('RND VS PROFIT SEABORN')
plt.grid()
plt.show()

image.png

Categorical and Continuous features seperation¶

In [11]:
df.columns
Out[11]:
Index(['RND', 'ADMIN', 'MKT', 'STATE', 'PROFIT'], dtype='object')
In [12]:
df.dtypes
Out[12]:
RND       float64
ADMIN     float64
MKT       float64
STATE      object
PROFIT    float64
dtype: object
In [13]:
df[df['PROFIT']>150000]
Out[13]:
RND ADMIN MKT STATE PROFIT
0 165349.20 136897.80 471784.10 New York 192261.83
1 162597.70 151377.59 443898.53 California 191792.06
2 153441.51 101145.55 407934.54 Florida 191050.39
3 144372.41 118671.85 383199.62 New York 182901.99
4 142107.34 91391.77 366168.42 Florida 166187.94
5 131876.90 99814.71 362861.36 New York 156991.12
6 134615.46 147198.87 127716.82 California 156122.51
7 130298.13 145530.06 323876.68 Florida 155752.60
8 120542.52 148718.95 311613.29 New York 152211.77
In [14]:
cat=(df.columns[df.dtypes=='object'])
cat
Out[14]:
Index(['STATE'], dtype='object')
In [15]:
#### if u dont want above output n want output only in list use--
cat=list(df.columns[df.dtypes=='object'])
cat
Out[15]:
['STATE']
In [16]:
####sometimes the number form can be int , float n all so just use != sign 
con=list(df.columns[df.dtypes!='object'])
con
Out[16]:
['RND', 'ADMIN', 'MKT', 'PROFIT']

image.png

In [17]:
df['STATE'].value_counts()
Out[17]:
New York      17
California    17
Florida       16
Name: STATE, dtype: int64
In [18]:
df['STATE'].value_counts().plot(kind='bar', title='Countplot for State' ,figsize=(16,8))
plt.show()
In [19]:
df['STATE'].value_counts().plot(kind='pie' , title='Countplot for State Pie',figsize=(16,8))
plt.show()

If multiple variables present in cat then apply for loop¶

In [20]:
cat
Out[20]:
['STATE']
In [21]:
for i in cat:
    df[i].value_counts().plot(kind ='bar', title =f'Countplot for {i}' , figsize=(16,8)) 
    plt.show()

Continuous features - Histogram¶

In [22]:
con
Out[22]:
['RND', 'ADMIN', 'MKT', 'PROFIT']

Here KDE -> Kernel Density Estimate¶

In [23]:
plt.figure(figsize=(16,8))
sns.histplot(data=df, x= 'ADMIN' , kde=True)
plt.title('Histogram for Admin')
plt.grid()
plt.show()

Apply for loop on con variables¶

In [24]:
con
Out[24]:
['RND', 'ADMIN', 'MKT', 'PROFIT']
In [25]:
for i in con:
    plt.figure(figsize=(16,8))
    sns.histplot(data=df, x=i , kde=True)
    plt.title(f'Histogram for {i}')
    ####dont add grid() option in histogram as it dosent look proper n good ..can add only in scatter plot
    plt.show()

Complete EDA (Exploratory Data Analysis)¶

image.png

Step 1: Reading the dataset¶

In [26]:
import pandas as pd
df=pd.read_csv('Cars93 (1).csv')
df.head()
Out[26]:
id Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags ... Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make
0 1 Acura Integra Small 12.9 15.9 18.8 25 31 None ... 5 177 102 68 37 26.5 11.0 2705 non-USA Acura Integra
1 2 Acura Legend Midsize 29.2 33.9 38.7 18 25 Driver & Passenger ... 5 195 115 71 38 30.0 15.0 3560 non-USA Acura Legend
2 3 Audi 90 Compact 25.9 29.1 32.3 20 26 Driver only ... 5 180 102 67 37 28.0 14.0 3375 non-USA Audi 90
3 4 Audi 100 Midsize 30.8 37.7 44.6 19 26 NaN ... 6 193 106 70 37 31.0 17.0 3405 non-USA Audi 100
4 5 BMW 535i Midsize 23.7 30.0 36.2 22 30 Driver only ... 4 186 109 69 39 27.0 13.0 3640 non-USA BMW 535i

5 rows × 28 columns

Step 2: Get Basic details of dataframe¶

In [27]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 93 entries, 0 to 92
Data columns (total 28 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   id                  93 non-null     int64  
 1   Manufacturer        93 non-null     object 
 2   Model               93 non-null     object 
 3   Type                93 non-null     object 
 4   Min.Price           93 non-null     float64
 5   Price               93 non-null     float64
 6   Max.Price           93 non-null     float64
 7   MPG.city            93 non-null     int64  
 8   MPG.highway         93 non-null     int64  
 9   AirBags             89 non-null     object 
 10  DriveTrain          93 non-null     object 
 11  Cylinders           93 non-null     object 
 12  EngineSize          93 non-null     float64
 13  Horsepower          93 non-null     int64  
 14  RPM                 93 non-null     int64  
 15  Rev.per.mile        93 non-null     int64  
 16  Man.trans.avail     93 non-null     object 
 17  Fuel.tank.capacity  93 non-null     float64
 18  Passengers          93 non-null     int64  
 19  Length              93 non-null     int64  
 20  Wheelbase           93 non-null     int64  
 21  Width               93 non-null     int64  
 22  Turn.circle         93 non-null     int64  
 23  Rear.seat.room      91 non-null     float64
 24  Luggage.room        82 non-null     float64
 25  Weight              93 non-null     int64  
 26  Origin              93 non-null     object 
 27  Make                93 non-null     object 
dtypes: float64(7), int64(12), object(9)
memory usage: 20.5+ KB
In [28]:
s=df.isna().sum()
s
Out[28]:
id                     0
Manufacturer           0
Model                  0
Type                   0
Min.Price              0
Price                  0
Max.Price              0
MPG.city               0
MPG.highway            0
AirBags                4
DriveTrain             0
Cylinders              0
EngineSize             0
Horsepower             0
RPM                    0
Rev.per.mile           0
Man.trans.avail        0
Fuel.tank.capacity     0
Passengers             0
Length                 0
Wheelbase              0
Width                  0
Turn.circle            0
Rear.seat.room         2
Luggage.room          11
Weight                 0
Origin                 0
Make                   0
dtype: int64
In [29]:
s[s>0]
Out[29]:
AirBags            4
Rear.seat.room     2
Luggage.room      11
dtype: int64

Checking duplicate values in dataframe¶

In [30]:
df.duplicated().sum()
Out[30]:
0

Step 3: Descriptive analytics¶

Drop statistically insignificant column (id)¶

In [31]:
df=df.drop(columns=['id'])
df.head()
Out[31]:
Manufacturer Model Type Min.Price Price Max.Price MPG.city MPG.highway AirBags DriveTrain ... Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight Origin Make
0 Acura Integra Small 12.9 15.9 18.8 25 31 None Front ... 5 177 102 68 37 26.5 11.0 2705 non-USA Acura Integra
1 Acura Legend Midsize 29.2 33.9 38.7 18 25 Driver & Passenger Front ... 5 195 115 71 38 30.0 15.0 3560 non-USA Acura Legend
2 Audi 90 Compact 25.9 29.1 32.3 20 26 Driver only Front ... 5 180 102 67 37 28.0 14.0 3375 non-USA Audi 90
3 Audi 100 Midsize 30.8 37.7 44.6 19 26 NaN Front ... 6 193 106 70 37 31.0 17.0 3405 non-USA Audi 100
4 BMW 535i Midsize 23.7 30.0 36.2 22 30 Driver only Rear ... 4 186 109 69 39 27.0 13.0 3640 non-USA BMW 535i

5 rows × 27 columns

Cat , Con Seperation¶

In [32]:
list(df.columns)
Out[32]:
['Manufacturer',
 'Model',
 'Type',
 'Min.Price',
 'Price',
 'Max.Price',
 'MPG.city',
 'MPG.highway',
 'AirBags',
 'DriveTrain',
 'Cylinders',
 'EngineSize',
 'Horsepower',
 'RPM',
 'Rev.per.mile',
 'Man.trans.avail',
 'Fuel.tank.capacity',
 'Passengers',
 'Length',
 'Wheelbase',
 'Width',
 'Turn.circle',
 'Rear.seat.room',
 'Luggage.room',
 'Weight',
 'Origin',
 'Make']
In [33]:
df.dtypes
Out[33]:
Manufacturer           object
Model                  object
Type                   object
Min.Price             float64
Price                 float64
Max.Price             float64
MPG.city                int64
MPG.highway             int64
AirBags                object
DriveTrain             object
Cylinders              object
EngineSize            float64
Horsepower              int64
RPM                     int64
Rev.per.mile            int64
Man.trans.avail        object
Fuel.tank.capacity    float64
Passengers              int64
Length                  int64
Wheelbase               int64
Width                   int64
Turn.circle             int64
Rear.seat.room        float64
Luggage.room          float64
Weight                  int64
Origin                 object
Make                   object
dtype: object
In [34]:
cat=list(df.columns[df.dtypes==object])
con=list(df.columns[df.dtypes!=object])
In [35]:
cat
Out[35]:
['Manufacturer',
 'Model',
 'Type',
 'AirBags',
 'DriveTrain',
 'Cylinders',
 'Man.trans.avail',
 'Origin',
 'Make']
In [36]:
con
Out[36]:
['Min.Price',
 'Price',
 'Max.Price',
 'MPG.city',
 'MPG.highway',
 'EngineSize',
 'Horsepower',
 'RPM',
 'Rev.per.mile',
 'Fuel.tank.capacity',
 'Passengers',
 'Length',
 'Wheelbase',
 'Width',
 'Turn.circle',
 'Rear.seat.room',
 'Luggage.room',
 'Weight']
In [37]:
df[cat].describe().T
Out[37]:
count unique top freq
Manufacturer 93 32 Chevrolet 8
Model 93 93 Integra 1
Type 93 6 Midsize 22
AirBags 89 3 Driver only 42
DriveTrain 93 3 Front 67
Cylinders 93 6 4 49
Man.trans.avail 93 2 Yes 61
Origin 93 2 USA 48
Make 93 93 Acura Integra 1
In [38]:
df['Cylinders'].value_counts()
Out[38]:
4         49
6         31
8          7
3          3
5          2
rotary     1
Name: Cylinders, dtype: int64
In [39]:
df['Origin'].value_counts()
Out[39]:
USA        48
non-USA    45
Name: Origin, dtype: int64
In [40]:
df[con].describe().T
Out[40]:
count mean std min 25% 50% 75% max
Min.Price 93.0 17.125806 8.746029 6.7 10.8 14.7 20.3 45.4
Price 93.0 19.509677 9.659430 7.4 12.2 17.7 23.3 61.9
Max.Price 93.0 21.898925 11.030457 7.9 14.7 19.6 25.3 80.0
MPG.city 93.0 22.365591 5.619812 15.0 18.0 21.0 25.0 46.0
MPG.highway 93.0 29.086022 5.331726 20.0 26.0 28.0 31.0 50.0
EngineSize 93.0 2.667742 1.037363 1.0 1.8 2.4 3.3 5.7
Horsepower 93.0 143.827957 52.374410 55.0 103.0 140.0 170.0 300.0
RPM 93.0 5280.645161 596.731690 3800.0 4800.0 5200.0 5750.0 6500.0
Rev.per.mile 93.0 2332.204301 496.506525 1320.0 1985.0 2340.0 2565.0 3755.0
Fuel.tank.capacity 93.0 16.664516 3.279370 9.2 14.5 16.4 18.8 27.0
Passengers 93.0 5.086022 1.038979 2.0 4.0 5.0 6.0 8.0
Length 93.0 183.204301 14.602382 141.0 174.0 183.0 192.0 219.0
Wheelbase 93.0 103.946237 6.819674 90.0 98.0 103.0 110.0 119.0
Width 93.0 69.376344 3.778986 60.0 67.0 69.0 72.0 78.0
Turn.circle 93.0 38.956989 3.223265 32.0 37.0 39.0 41.0 45.0
Rear.seat.room 91.0 27.829670 2.989072 19.0 26.0 27.5 30.0 36.0
Luggage.room 82.0 13.890244 2.997967 6.0 12.0 14.0 15.0 22.0
Weight 93.0 3072.903226 589.896510 1695.0 2620.0 3040.0 3525.0 4105.0

Step 4 : Univariate Analysis¶

image.png

In [41]:
import matplotlib.pyplot as plt
import seaborn as sns
In [42]:
cat
Out[42]:
['Manufacturer',
 'Model',
 'Type',
 'AirBags',
 'DriveTrain',
 'Cylinders',
 'Man.trans.avail',
 'Origin',
 'Make']
In [43]:
for i in cat:
    df[i].value_counts().plot(kind='bar',
                              title=f'Countplot for {i}',
                              figsize=(16,8))
    plt.show()
In [44]:
con
Out[44]:
['Min.Price',
 'Price',
 'Max.Price',
 'MPG.city',
 'MPG.highway',
 'EngineSize',
 'Horsepower',
 'RPM',
 'Rev.per.mile',
 'Fuel.tank.capacity',
 'Passengers',
 'Length',
 'Wheelbase',
 'Width',
 'Turn.circle',
 'Rear.seat.room',
 'Luggage.room',
 'Weight']
In [45]:
for i in con:
    plt.figure(figsize=(16,8))
    sns.histplot(data=df , x=i , kde=True)
    plt.title(f'Histogram for {i}')
    plt.show()

Bivariate Analysis - Analysis of 2 variables together¶

image.png

In [46]:
con
Out[46]:
['Min.Price',
 'Price',
 'Max.Price',
 'MPG.city',
 'MPG.highway',
 'EngineSize',
 'Horsepower',
 'RPM',
 'Rev.per.mile',
 'Fuel.tank.capacity',
 'Passengers',
 'Length',
 'Wheelbase',
 'Width',
 'Turn.circle',
 'Rear.seat.room',
 'Luggage.room',
 'Weight']
In [47]:
plt.figure(figsize=(16,8))
sns.scatterplot(data=df , x='Length' , y='Price')
plt.title('Scatterplot for Length VS Price')
plt.grid()
plt.show()

Same question through matplotlib library¶

In [48]:
plt.figure(figsize=(16,8))
plt.scatter(df['Length'], df['Price'])
plt.xlabel('Length')
plt.ylabel('Price')
plt.title('Length VS Price')
plt.grid()
plt.show()

Correlation heatmap¶

In [49]:
corr=df[con].corr()
corr
Out[49]:
Min.Price Price Max.Price MPG.city MPG.highway EngineSize Horsepower RPM Rev.per.mile Fuel.tank.capacity Passengers Length Wheelbase Width Turn.circle Rear.seat.room Luggage.room Weight
Min.Price 1.000000 0.970601 0.906756 -0.622875 -0.579966 0.645488 0.802444 -0.042598 -0.470395 0.635369 0.061236 0.553859 0.516758 0.492878 0.428603 0.376642 0.413485 0.666554
Price 0.970601 1.000000 0.981580 -0.594562 -0.560680 0.597425 0.788218 -0.004955 -0.426395 0.619480 0.057860 0.503628 0.500864 0.456028 0.392590 0.311499 0.366569 0.647179
Max.Price 0.906756 0.981580 1.000000 -0.547811 -0.522561 0.535012 0.744445 0.025015 -0.374024 0.581294 0.053216 0.442933 0.467501 0.408414 0.347785 0.247260 0.315315 0.605142
MPG.city -0.622875 -0.594562 -0.547811 1.000000 0.943936 -0.710003 -0.672636 0.363045 0.695857 -0.813144 -0.416856 -0.666239 -0.667108 -0.720534 -0.666389 -0.384347 -0.494894 -0.843139
MPG.highway -0.579966 -0.560680 -0.522561 0.943936 1.000000 -0.626795 -0.619044 0.313469 0.587497 -0.786039 -0.466386 -0.542897 -0.615384 -0.640359 -0.593683 -0.366684 -0.371629 -0.810658
EngineSize 0.645488 0.597425 0.535012 -0.710003 -0.626795 1.000000 0.732120 -0.547898 -0.824009 0.759306 0.372721 0.780283 0.732484 0.867110 0.778464 0.502750 0.680827 0.845075
Horsepower 0.802444 0.788218 0.744445 -0.672636 -0.619044 0.732120 1.000000 0.036688 -0.600314 0.711790 0.009264 0.550865 0.486854 0.644413 0.561216 0.256732 0.359217 0.738798
RPM -0.042598 -0.004955 0.025015 0.363045 0.313469 -0.547898 0.036688 1.000000 0.494764 -0.333345 -0.467138 -0.441249 -0.467812 -0.539721 -0.505651 -0.342175 -0.524845 -0.427931
Rev.per.mile -0.470395 -0.426395 -0.374024 0.695857 0.587497 -0.824009 -0.600314 0.494764 1.000000 -0.609710 -0.334976 -0.690233 -0.636824 -0.780460 -0.733160 -0.377010 -0.592792 -0.735264
Fuel.tank.capacity 0.635369 0.619480 0.581294 -0.813144 -0.786039 0.759306 0.711790 -0.333345 -0.609710 1.000000 0.472095 0.690461 0.757674 0.798719 0.671343 0.509689 0.613437 0.894018
Passengers 0.061236 0.057860 0.053216 -0.416856 -0.466386 0.372721 0.009264 -0.467138 -0.334976 0.472095 1.000000 0.485294 0.694054 0.489979 0.449025 0.694134 0.653317 0.553273
Length 0.553859 0.503628 0.442933 -0.666239 -0.542897 0.780283 0.550865 -0.441249 -0.690233 0.690461 0.485294 1.000000 0.823650 0.822148 0.738955 0.549958 0.712962 0.806274
Wheelbase 0.516758 0.500864 0.467501 -0.667108 -0.615384 0.732484 0.486854 -0.467812 -0.636824 0.757674 0.694054 0.823650 1.000000 0.807213 0.723324 0.667259 0.734127 0.871895
Width 0.492878 0.456028 0.408414 -0.720534 -0.640359 0.867110 0.644413 -0.539721 -0.780460 0.798719 0.489979 0.822148 0.807213 1.000000 0.817854 0.465618 0.673490 0.874961
Turn.circle 0.428603 0.392590 0.347785 -0.666389 -0.593683 0.778464 0.561216 -0.505651 -0.733160 0.671343 0.449025 0.738955 0.723324 0.817854 1.000000 0.466328 0.585018 0.778043
Rear.seat.room 0.376642 0.311499 0.247260 -0.384347 -0.366684 0.502750 0.256732 -0.342175 -0.377010 0.509689 0.694134 0.549958 0.667259 0.465618 0.466328 1.000000 0.651968 0.526250
Luggage.room 0.413485 0.366569 0.315315 -0.494894 -0.371629 0.680827 0.359217 -0.524845 -0.592792 0.613437 0.653317 0.712962 0.734127 0.673490 0.585018 0.651968 1.000000 0.637226
Weight 0.666554 0.647179 0.605142 -0.843139 -0.810658 0.845075 0.738798 -0.427931 -0.735264 0.894018 0.553273 0.806274 0.871895 0.874961 0.778043 0.526250 0.637226 1.000000
In [50]:
### ya data=df nhi likhte directly corelation pass karte haii
plt.figure(figsize=(25,25))
sns.heatmap(corr,annot=True)  ### here annot displays the data value 
plt.show()
In [51]:
plt.figure(figsize=(25,25))
plt.heatmap(corr,annot=True)
plt.show()  #################### here graph is not plot because mathplotlib dosent follow heatmap 

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[51], line 2
      1 plt.figure(figsize=(25,25))
----> 2 plt.heatmap(corr,annot=True)
      3 plt.show()

AttributeError: module 'matplotlib.pyplot' has no attribute 'heatmap'
<Figure size 2500x2500 with 0 Axes>

image.png

image.png

In [52]:
cat
Out[52]:
['Manufacturer',
 'Model',
 'Type',
 'AirBags',
 'DriveTrain',
 'Cylinders',
 'Man.trans.avail',
 'Origin',
 'Make']
In [53]:
con
Out[53]:
['Min.Price',
 'Price',
 'Max.Price',
 'MPG.city',
 'MPG.highway',
 'EngineSize',
 'Horsepower',
 'RPM',
 'Rev.per.mile',
 'Fuel.tank.capacity',
 'Passengers',
 'Length',
 'Wheelbase',
 'Width',
 'Turn.circle',
 'Rear.seat.room',
 'Luggage.room',
 'Weight']
In [54]:
plt.figure(figsize=(16,8))
sns.boxplot(data=df , x='Type', y='Price')
plt.title('Type VS Price')
plt.show()
In [55]:
plt.figure(figsize=(16,8))
sns.boxplot(data=df, x='Price', y='AirBags')
plt.title('Boxplot for AirBags vs Price')
plt.show()
In [56]:
for i in cat:
    plt.figure(figsize=(16,8))
    sns.boxplot(data=df, x=i, y='Price')
    plt.title(f'Box plot for Price VS {i} ')
    plt.show()
In [57]:
for i in cat:
    plt.figure(figsize=(16,8))
    sns.boxplot(data=df, x=i, y='MPG.city')
    plt.title(f'Boxplot for {i} vs MPG.city')
    plt.show()

Categorical vs Cateogorical¶

In [58]:
cat
Out[58]:
['Manufacturer',
 'Model',
 'Type',
 'AirBags',
 'DriveTrain',
 'Cylinders',
 'Man.trans.avail',
 'Origin',
 'Make']
In [59]:
cr=pd.crosstab(df['Type'],df['Cylinders'])
cr
Out[59]:
Cylinders 3 4 5 6 8 rotary
Type
Compact 0 15 0 1 0 0
Large 0 0 0 7 4 0
Midsize 0 7 1 12 2 0
Small 3 18 0 0 0 0
Sporty 0 8 0 4 1 1
Van 0 1 1 7 0 0
In [60]:
sns.heatmap(cr,annot=True)
Out[60]:
<Axes: xlabel='Cylinders', ylabel='Type'>
In [61]:
con
Out[61]:
['Min.Price',
 'Price',
 'Max.Price',
 'MPG.city',
 'MPG.highway',
 'EngineSize',
 'Horsepower',
 'RPM',
 'Rev.per.mile',
 'Fuel.tank.capacity',
 'Passengers',
 'Length',
 'Wheelbase',
 'Width',
 'Turn.circle',
 'Rear.seat.room',
 'Luggage.room',
 'Weight']
In [62]:
cat
Out[62]:
['Manufacturer',
 'Model',
 'Type',
 'AirBags',
 'DriveTrain',
 'Cylinders',
 'Man.trans.avail',
 'Origin',
 'Make']
In [63]:
cr1=pd.crosstab(df['Man.trans.avail'],df['DriveTrain'])
cr1
Out[63]:
DriveTrain 4WD Front Rear
Man.trans.avail
No 3 22 7
Yes 7 45 9
In [64]:
sns.heatmap(cr1,annot=True)
Out[64]:
<Axes: xlabel='DriveTrain', ylabel='Man.trans.avail'>

Mutlivariate pairplot¶

In [67]:
sns.pairplot(data=df)
Out[67]:
<seaborn.axisgrid.PairGrid at 0x25a87274750>
In [68]:
sns.pairplot(data=df,hue='Type')
Out[68]:
<seaborn.axisgrid.PairGrid at 0x25a9e431150>
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In [ ]:
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